Gene expression cartography
Gene expression cartography
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DOI：
10.1038/s41586-019-1773-3
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发表时间：
2019-12-05
期刊：
影响因子：
64.8
通讯作者：
Rajewsky, Nikolaus
中科院分区：
文献类型：
--
作者：
Nitzan, Mor;Karaiskos, Nikos;Rajewsky, Nikolaus
单个细胞中的多重 RNA 测序正在改变基础和临床生命科学(1-4)。然而，通常情况下，组织必须首先被解离，并且因此丢失了有关细胞之间空间关系和通信的关键信息。重建组织的现有方法通过使用通常不存在的标记基因 (5,6) 表达的空间模式，独立于其他细胞为每个细胞分配空间位置。在这里，我们在很少或没有先验知识的情况下重建空间位置，通过搜索已测序细胞的空间排列，其中附近的细胞具有的转录谱通常（但不总是）比相距较远的细胞更相似。我们将此任务表述为概率嵌入的广义最优传输问题，并推导了一种有效的迭代算法来解决它。我们重建了哺乳动物肝脏和肠上皮、苍蝇和斑马鱼胚胎、哺乳动物小脑和整个肾脏切片中基因的空间表达，并使用重建的组织来识别具有空间信息的基因。因此，我们确定了动物组织中基因空间表达的组织原则，可用于推断单个细胞空间位置的有意义的概率。我们的框架（“novoSpaRc”）可以合并先前的空间信息，并且与任何单细胞技术兼容。可以使用我们的方法来测试基因表达制图的其他原理。
Multiplexed RNA sequencing in individual cells is transforming basic and clinical life sciences(1-4). Often, however, tissues must first be dissociated, and crucial information about spatial relationships and communication between cells is thus lost. Existing approaches to reconstruct tissues assign spatial positions to each cell, independently of other cells, by using spatial patterns of expression of marker genes(5,6)-which often do not exist. Here we reconstruct spatial positions with little or no prior knowledge, by searching for spatial arrangements of sequenced cells in which nearby cells have transcriptional profiles that are often (but not always) more similar than cells that are farther apart. We formulate this task as a generalized optimal-transport problem for probabilistic embedding and derive an efficient iterative algorithm to solve it. We reconstruct the spatial expression of genes in mammalian liver and intestinal epithelium, fly and zebrafish embryos, sections from the mammalian cerebellum and whole kidney, and use the reconstructed tissues to identify genes that are spatially informative. Thus, we identify an organization principle for the spatial expression of genes in animal tissues, which can be exploited to infer meaningful probabilities of spatial position for individual cells. Our framework ('novoSpaRc') can incorporate prior spatial information and is compatible with any single-cell technology. Additional principles that underlie the cartography of gene expression can be tested using our approach.